Daniel Heinz
About Daniel Heinz
Daniel Heinz is a Data Scientist at Arity, specializing in inference techniques such as Markov Chain Monte Carlo and Variational Inference. He holds a PhD in Statistics from Carnegie Mellon University and has extensive experience in modeling theory, information retrieval, and biostatistics.
Work at Arity
Daniel Heinz has been employed at Arity as a Data Scientist since 2019. In this role, he applies various inference techniques, including Markov Chain Monte Carlo and Variational Inference, to analyze data and develop models. His work contributes to the company's focus on leveraging data for insights in the Greater Chicago Area.
Education and Expertise
Daniel Heinz holds a Bachelor of Arts degree in Psychology and Math from Haverford College, which he attended from 1998 to 2002. He furthered his education at Carnegie Mellon University, where he earned both a Master of Science and a PhD in Statistics, completing his studies from 2004 to 2010. His academic background provides a strong foundation for his expertise in Hierarchical Bayesian Models and Mixed-Membership Models.
Background
Before joining Arity, Daniel Heinz worked in academia. He served as an Assistant Professor at Loyola University Maryland from 2011 to 2014 and as a Lecturer at the University of Chicago from 2009 to 2011. His experience in these roles contributed to his knowledge in modeling theory, information retrieval, and biostatistics.
Programming and Technical Skills
Daniel Heinz possesses extensive programming skills, particularly in R and C++. He also has experience with macro programming in MSOffice and OpenOffice. Additionally, he is familiar with other programming languages and tools, including Perl, XML, and SAS, which enhance his capabilities in data analysis and modeling.
Research Projects and Contributions
Throughout his career, Daniel Heinz has worked on various projects that focus on modeling theory, information retrieval, and biostatistics. His research contributions reflect his strong analytical skills and his ability to apply statistical methods to real-world problems.